Corporations often utilize web services, databases, and other remote hosting systems to exchange electronic information with customers, clients, and other organizations. For example, financial institutions, such as banks and investment advisors, often provide web services that allow direct communication between the financial institution and its customers. Individual web services instances are interoperable and can be used in combination with other web services instances to perform complex transactions. To meet ever-changing business needs, these systems must be adaptable. Further, they must be able to incorporate, organize, and present new information in a meaningful way.
Often, a business analyst identifies a need for new or augmented functionality in the remote hosting systems to provide a better product or service to the corporation's customers. The business analyst typically creates a business case report that must be interpreted and translated by a technical analyst, who then generates a data object that can be incorporated in the existing system to provide the desired functionality. This can be a time intensive and laborious process. Moreover, business analysts and technical analyst often do not “speak the same language,” so to speak, and frequently present information to one another in a way that inhibits satisfying business needs and technical needs at the same time.
Some organizations have created a manual throughput process in an attempt to streamline the creation or modification of the data object. For example, a business analyst usually creates a set of sample data that is pertinent to the desired functionality. A computerized system is then used to develop an object-relation model that extracts concepts from the sample data. Next, a technical analyst creates an ontology based on the object-relation model to aid in the creation of the data object. The ontology is then used to create or modify the data object. While this process does provide some efficiencies, it is still labor intensive.
Accordingly, there is an essential need for a solution that can overcome the deficiencies of the prior art, whereby a data object can be updated or created by the automatic utilization of semantic modeling to create an ontology.